reL Re: st: Interpreting mediation model sobel goodman test

["Ariel Linden, DrPH" <ariel.linden@gmail.com>]

Hi John,

While this is a relatively old thread (in statalist time a month is like a
century), I am revisiting your code below and have a question. In your
-reg3- equation and subsequent nlcom, you recover the "total effect". How
would you recover the direct and indirect effects using -reg3-? 

In a separate set of postings dated Feb 2009, Maarten laid out an approach
using -sureg-, but it doesn't appear that the thread ever came back to
-reg3- . The primary issue here is that one would need to have an outcome
model containing both the mediator (m) and treatment variable (x), in order
to derive the direct effect of x on y. The -reg3- model below for the
outcome does not contain the x variable (x is treated as exogenous).

Thanks

Ariel  

From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of John
Antonakis
Sent: Tuesday, October 18, 2011 12:11 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Interpreting mediation model sobel goodman test

Hi Meredith:

I assume you used the -sgmediation- package; I would not use this 
routine UNLESS your mediator is exogenous (and you are sure of this). If

it is endogenous sgmedation will give you inconsistent estimates (it 
estimates the system of equations with OLS, and uses the dated 
Baron-Kenny methods); you do not tackle the endogeneity problem with 
sgmediation.  You need to estimate your system of equations with an 
instrumental-variable estimator (e.g., 2SLS).

Take a look at this podcast, where I discuss this problem in detail:

Endogeneity: An inconvenient truth (full version) (about 32 minutes in 
length)
http://www.youtube.com/watch?v=dLuTjoYmfXs

If you just want the nitty gritty see:

Endogeneity: An inconvenient truth (for researchers)
(Excludes the "gentle introduction" content and discusses the two-stage 
least squares estimator straight away; about 16 minutes in length)
http://www.youtube.com/watch?v=yi_5M7oUceE

See also:

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (submitted). 
Causality and endogeneity: Problems and solutions. In D.V. Day (Ed.), 
The Oxford Handbook of Leadership and Organizations.
http://www.hec.unil.ch/jantonakis/Causality_and_endogeneity_final.pdf

To understand exactly the nature of the problem run the following code, 
where x is endogenous with respect to y:

clear
set seed 123
set obs 1000
gen x = rnormal()
gen e = rnormal()
gen m = e + .5*x + rnormal()
gen y = .5*m - e + rnormal()
reg3 (y = m) (m = x), 2sls
nlcom [m]x*[y]m
sgmediation y, mv(m) iv(x)

 From the above model, we have an instrument x, an endogenous regressor 
m, and omitted cause e, and a dependent variable y. We know that the 
indirect effect of x on y is .5*.5=.25. 2SLS recovers this parameter 
well (.24, p>.001). However, the sgmediation program gives .03 (and p = 
.04).


Now, let's rerun this to see when you'd get the same results with 
sgmediation (if x is exogenous with respect to y):

clear
set seed 123
set obs 1000
gen x = rnormal()
gen e = rnormal()
gen m = .5*x + rnormal()
gen y = .5*m + rnormal()
reg3 (y = m) (m = x), 2sls
nlcom [m]x*[y]m
reg3 (y = m) (m = x), ols
nlcom [m]x*[y]m
sgmediation y, mv(m) iv(x)

Notice that the 2SLS model is still consistent (but less efficient). The

OLS estimator and sgmediation pretty much give the same estimates and 
standard errors.

HTH,
John.



__________________________________________

Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________


On 18.10.2011 19:41, Meredith T. Niles wrote:
 > Hello all,
 >   I am working on running multiple and single mediation models to
assess
 > farmer climate change perceptions and potential adoption of climate
 > change practices.  I am getting an odd result when running a Sobel
 > goodman test in Stata with regards to the portion of total effect
that
 > is mediated (5.139).  Does anyone have any perspective on why this
 > number is so large?  Running the same test with another set of
climate
 > change practices yields a proportion of total effect that is mediated
at
 > 0.79 which seems much more in line with other results I've seen.
 >
 >
 > Sobel-Goodman Mediation Tests
 >
 >              Coef         Std Err     Z           P>|Z|
 > Sobel       -.09959383    .05075882  -1.962      .04975096
 > Goodman-1   -.09959383    .05217108  -1.909      .05626401
 > Goodman-2   -.09959383    .04930612   -2.02      .04339293
 >
 > Indirect effect = -.09959383
 >   Direct effect = .08021537
 >    Total effect = -.01937846
 >
 > Proportion of total effect that is mediated:  5.1394091
 > Ratio of indirect to direct effect:      -1.2415804
 >
 >
 > Thanks for your thoughts.
 >
 > Best,
 > Meredith Niles
 >
 >
 > PhD Candidate, Graduate Group in Ecology
 > NSF REACH IGERT Trainee
 > Deputy External Chair, Graduate Student Association
 > University of California, Davis
 > 2126 Wickson

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/